Optimizing crop planting structures under the influence of climate change and human activities is crucial for sustainable food production and global food security. Taking the Naoli River Basin in ...Northeast China as a case area, a machine learning model based on maximum entropy was used to explore the suitability distribution of crops under the influence of both environmental factors and human activities. The optimized planting structure strategies were tested in combination with future climate change. The results show that considering human activities can more accurately simulate crop suitability than considering only natural environmental factors. The suitable planting areas for maize, rice, and soybeans are 18,553.54 km2, 10,335.98 km2, and 5844.80 km2, respectively. Highly adapted areas for major crops are concentrated in the plain areas of the middle reaches of the river basin, rather than in populated areas, and there are overlaps among the suitable planting areas for each crop. The optimal crop distribution for the planting structure is to plant rice in the hydrophilic areas of the plain hinterland, soybeans in the plain hinterland farther from the water source, and corn in the peripheral plains and gently sloping mountainous areas. Human activities exerted a strong influence on the potential scatter of soybeans, while climate change had the most significant implications for maize. Future climate change may reduce the area of suitable crop zones, posing challenges to regional food production. It is necessary to reflect on how to rationally balance soil and water resources, as well as how to cope with climate change in the future.
•Employ projection pursuit method for a thorough evaluation of China’s UC.•The CCD between UC and GTFP in China shows an increasing trend.•Northeast China’s UC and GTFP coordination fluctuates, ...reflecting divergence and convergence.•Government intervention plays a crucial role in UC and GTFP’s coordinated development.
The coordinated development of urban competitiveness (UC) and green total factor productivity (GTFP) is paramount for achieving global sustainable development objectives, especially for regional sustainability in China. This study utilizes panel data from 2011 to 2019 encompassing 273 Chinese cities to evaluate the urban competitiveness index employing the projection pursuit method. The research categorizes Chinese cities into four regions—East, Central, West, and Northeast—for comparative analysis purposes. The findings unveil: (1) a discernible rise in both overall and regional coordination between urban competitiveness and green total factor productivity in China; (2) throughout the study period, except for fluctuations observed in Northeastern China, coordination in other regions sustained relative stability, manifesting a trend of divergence-convergence; (3) the coordination between urban competitiveness and green total factor productivity across all regions demonstrated β-convergence, with Central China exhibiting the swiftest convergence and Northeastern China the slowest; (4) taking into account factors such as government intervention, industrial structure, and human capital, there is a likelihood that the growth rate of coordination between urban competitiveness and green total factor productivity in diverse regions will converge towards a synergistic trend. Therefore, as Chinese cities strive to enhance competitiveness and green total factor productivity, governmental attention should be directed towards discerning the developmental trends and influencing factors affecting their coordination. Such an approach will facilitate the realization of regional sustainable development objectives while propelling the synchronized advancement of urban competitiveness and green total factor productivity.
Venture capital plays a vital role in boosting economic growth by providing an inexhaustible impetus for economic innovation and development. We use all the joint venture capital events of Chinese ...listed companies in the past 10 years to describe the characteristics of the joint venture capital network structure, identify the dynamic evolution characteristics of the community, and introduce random attacks and deliberate attacks to explore the resilience of joint venture capital cooperation. The study finds that the joint venture capital network in China has expanded in scale, with an increasing number of participants and a diversified investment industry. However, the connection between members within the network remains relatively loose, indicating fragmentation and a need to improve network quality. The community structure of core members is significant, with evident differences in scale. The network exhibits weak robustness, relying heavily on key enterprises and demonstrating a poor ability to resist external interference. The study proposes countermeasures and suggestions for optimizing the network structure of joint venture capital, aiming to enhance the environment and performance of joint venture capital and promote the high-quality development of China’s joint venture capital market.
The development of renewable energy is the key to tackling climate changes and ensuring energy security. Meanwhile, the process is usually associated with a huge investment and financing gap, ...especially for transitional countries like China. Hence, developing and innovating the traditional financial system is urgent. However, existing studies have mainly focused on traditional financial perspectives, and few papers have critically examined how digital financial inclusion affects the growth of renewable energy industries. To this end, using Chinese provincial data from 2011 to 2020 and applying Feasible Generalized Least Squares (FGLS) method, this study explores the effect of financial inclusion on renewable energy and its underlying mechanisms from the production and consumption perspectives. It is found that the impact of financial inclusion on the production side of the renewable energy industry is significantly better than on the consumption side, and there is heterogeneity effect in different energy resource varieties, different regional locations, and different financial regulation intensities. The financial inclusion on renewable energy has a significant pulling effect in photovoltaic and wind provinces, but not in large hydropower provinces. Then, its driving effect is particularly pronounced in the northern region. Nonetheless, it is not significant for renewable energy consumption in the southern region. Besides, this positive effect cannot be achieved without stronger financial regulation. The mechanism of action assessment finds that optimizes the consumption pattern, increase the competition of commercial banks, and improve technological innovation all contribute to the positive effect whereas, FDI shows the negative effect. This study provides new evidence on China's achievement of the energy transition, suggesting the need to further improve incentives for FDI firms and to strengthen consumption-side guidance.
Financial development and demographic changes have an important impact on the non-hydro renewable energy industry, and this impact is significantly different in countries at different stages of ...development. This paper studies 10 developed countries and 10 developing countries using annual data from 1990 to 2018. Through the panel cointegration estimation method, the paper evaluates the impact of financial development and demographic changes on the non-hydro renewable energy industry. The empirical results show that the stock market and bank intermediary have a significant role in promoting the growth of the non-hydro renewable energy industry in developing and developed countries. The bond market has a significant positive impact on the growth of the non-hydro renewable energy industry in developed countries only. Foreign direct investment in developing countries significantly promotes the growth of the non-hydro renewable energy industry, while in developed countries it has a negative impact. Population aging has a significant inhibitory effect on the growth of the non-hydro renewable energy industry in developing and developed countries.
Against the backdrop of global climate change, industrial carbon emission reduction has become an important pathway to for global low-carbon development. This study constructs a framework of ...geographic spatial constraints regionalization and multi-objective machine learning to predict future industrial carbon emission efficiency (ICEE) and explore strategies for carbon emission reduction. Firstly, the ICEE of 285 Chinese cities were calculated by the super-efficiency slacks-based measure. Secondly, the cities were classified into four ICEE level regions through the spatially constrained multivariate clustering. Next, the multi-objective particle swarm optimization-BP (MOPSO-BP) model was constructed to predict the future trends of ICEE in the four regions. Finally, the geographical detector and multi-scale geographically weighted regression were employed for exploring driving force and carbon emission reduction strategies in different regions. The results show that most cities had low or medium ICEE, while super efficiency cities were mainly distributed in the east coastal areas. The prediction performance of the MOPSO-BP model for the four regions was better than the ordinary particle swarm optimization-BP and traditional BP model. Except for the Agricultural Production Region, there is considerable room for improving the ICEE of other regions over the next decade. Macroeconomic and microeconomic development have a global effect in promoting regional ICEE improvement, urban construction shows a promoting or inhibiting effect in different regions, and information technology has significant spatial heterogeneity in its influence within each region. The analysis framework developed in the study is a reliable solution for managing and planning ICEE and provides constructive suggestions for future regional low-carbon development.
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•MOPSO-BP is a reliable tool for predicting the future trend of ICEE.•The 285 cities were classified into four ICEE regions by spatial constraints.•In regions other than APR, cities meet challenges in achieving super efficiency.•The improvement strategies for ICEE are based on geographical detector and MGWR.•Proposing low-carbon development recommendations at regional and urban scales.
The urban competitiveness (UC) evaluation system is multidimensional and complex. This paper takes the simulated annealing (SA) model as the projection pursuit (PP) optimization to achieve a ...comprehensive assessment of competitiveness of 277 Chinese cities from 2011 to 2019, accompanied by energy saving and carbon-emission reduction (ESCER) as environmental measurements, to explore whether the two can meet the Porter hypothesis through coupling coordination degree (CCD). Further using spatiotemporal autocorrelation and obstacle degree model to uncover spatiotemporal features and interfering factors of coordinated development. Key findings include: (1) UC and ESCER show a slightly fluctuating upward trend during the research period, with apparent spatial variations. The eastern coastal region has a robust UC, while the less competitive central and western regions benefit from natural conditions, excelling in ESCER. (2) 87% of cities have achieved coordinated development between competitiveness and ESCER. Some coastal areas, often with a high CCD, are improving resource use efficiency and environmental benefits through economic agglomeration. From the perspective of the CCD collaboration network, the positive correlation accounts for about 85%, which reveals that most adjacent regions can cooperate on the road of coordinated development. (3) While differences exist in the coordinated development of UC-ESCER across various regions, social factors predominantly influence the obstacles affecting coordinated development. Specifically, a substantial barrier to the concordant progression of most cities is the number of patent applications, underscoring the pivotal role of innovation in aligning UC with ESCER.
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•Simulated annealing projection pursuit assessed Chinese cities' competitiveness.•There is spatial heterogeneity between UC and ESCER.•87% of cities achieve harmonious development for UC and ESCER.•Innovation is crucial for urban response to dual challenges of environment and economy.
Exploring the coupling coordination between China’s digital economy (DE) and industrial carbon emission efficiency (ICEE) is of great significance for achieving sustainable development goals. In the ...study, a multidimensional indicator system was established to evaluate DE, and spatiotemporal analysis and network analysis methods were used to reveal the dynamic evolution characteristics of DE and ICEE. The coupling coordination model and convergence model were adopted to explore the development trend of coupling coordination between DE and ICEE. The results show that the ICEE and DE in various provinces of China exhibit obvious spatial heterogeneity and spillover effects. Currently, the coupling coordination degree between the development of China’s DE and ICEE has reached the level of primary coordination or above. The coupling coordination degree between DE and ICEE in the eastern, central, and northeastern regions has reached an intermediate level or above, with the highest degree in the eastern region. The fluctuation of China’s ICEE has consistent
σ
-convergence and
β
-convergence, and the convergence effect is higher with the introduction of the DE than without it. The condition
β
-convergence result indicates that underdeveloped regions can narrow the gap between their ICEE and that of developed regions by utilizing their resource endowments, industrial structure, human capital, and other conditions, improving emission reduction measures and policies. This study provides a certain reference for the green and low-carbon development of industry in China and other developing countries in the digital economy era.
The Pearl River Basin (PRB) is of strategic importance to China's economic development and ecological sustainability, while carbon emissions (CES) are currently exerting immense pressure on the ...region. This study attempts to study the spatial-temporal evolution and driving factors of CES to achieve regional joint emission reduction. Through the integration of multi-source data, this study has developed a coupled model for calculating CES from both carbon sources and sinks. The space-time patterns and clustering characteristics of CES in the PRB were analyzed at multiple scales. And the main driving factors of CES were revealed from two scales of urban and basin, respectively. The results show that CES exhibited an increasing trend, with cities with high CES mainly located in the downstream of the PRB. The CES between neighboring cities exhibited strong spatial dynamics and dependence, resulting in a collaborative development trend of CES with adjacent cities. The social consumption level, land use degree, and fiscal expenditure were significant drivers of CES. Additionally, the interaction impact between influencing factors on CES exerted more significantly than any single factor. This study improved the methods of calculating CES with multi-source data and provided support for different-type cities to formulate targeted CES reduction policies.
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•A coupled model for calculating carbon emissions was developed based on multisource data.•The static and dynamic characteristics of carbon emissions were analyzed at multiple scales.•The drivers of carbon emissions were explored by integrating LMDI decomposition and geographical detector.•A strong positive synergistic evolution of urban carbon emissions was observed.•The interaction between influencing factors had a more significant influence on carbon emissions.
A method was developed for the simultaneous determination of 19 phthalate esters (PAEs) at trace level in cosmetics by solid phase extraction (SPE) purification and gas chromatography-mass ...spectrometry (GC-MS) detection. The PAEs were extracted from cosmetic samples by dichloromethane with ultrasonic-assisted technique, purified by an SPE column packed with silica gel and neutral alumina (2: 3, m/m) with the elution of 20 mL of mixed solvent of ethyl acetate-hexane (8: 2, v/v). Qualitative and quantitative analysis were carried out by GC-MS in full scan and selected ion monitoring modes. The retention time of quantitative ions and the abundance ratio of characteristic ions were applied to rapidly and accurately identify each analyte so as to prevent the occurring of possible mistakes from complex matrix intervention. Under optimized conditions, the average recoveries for a shampoo sample spiked with the standards at 0.1, 0.5, 2.0 microg/g were in the range of 72.2% and 110.9%, and the relative standard deviations (RSDs) for the 19 PAEs were less than 10.3% (n = 6) at the spiked level of 0.1 microg/g. The limits of detection (LODs, as 3 times of standard deviation) were between 0.0065 microg/g (for diisopentyl phthalate) and 0.062 microg/g (for diisobutyl phthalate). The method was successfully applied to the determination of the PAEs in 6 types of cosmetics. It is expected to promote the determination of the PAEs in other cosmetics with different matrices.
